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  • Writing white space to CSV fields in Python?

    - by matt
    When I try to write a field that includes whitespace in it, it gets split into multiple fields on the space. What's causing this? It's driving me insane. Thanks data = open("file.csv", "wb") w = csv.writer(data) w.writerow(['word1', 'word2']) w.writerow(['word 1', 'word2']) data.close() I'll get 2 fields(word1,word2) for first example and 3(word,1,word2) for the second.

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  • Writing csv header removes data from numpy array written below

    - by user338095
    I'm trying to export data to a csv file. It should contain a header (from datastack) and restacked arrays with my data (from datastack). One line in datastack has the same length as dataset. The code below works but it removes parts of the first line from datastack. Any ideas why that could be? s = ','.join(itertools.chain(dataset)) + '\n' newfile = 'export.csv' f = open(newfile,'w') f.write(s) numpy.savetxt(newfile, (numpy.transpose(datastack)), delimiter=', ') f.close()

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  • Python: Comparing specific columns in two csv files

    - by coder999
    Say that I have two CSV files (file1 and file2) with contents as shown below: file1: fred,43,Male,"23,45",blue,"1, bedrock avenue" file2: fred,39,Male,"23,45",blue,"1, bedrock avenue" I would like to compare these two CSV records to see if columns 0,2,3,4, and 5 are the same. I don't care about column 1. What's the most pythonic way of doing this? EDIT: Some example code would be appreciated.

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  • Cannot compile GDB7.8 with Python support

    - by j0h
    I am trying to install GDB7.8 with Python support. From the source folder, I am running ./configure --with-python When I did tab-complete from --with- I did not see Python in the list. But when I ran configure with that flag, it did not baulk. When I run make, it complains that Python is not found. checking for python2.7... no but Python is installed: $ which python python python2.7 python2.7-dbg-config python2 python2.7-dbg $ which python2.7 /usr/bin/python2.7 I compiled GDB without --with-python and things installed without error. I was under the impression that GDB7.8 had Python support without the need for special flags. But when I run: $gdb python (gdb) run test.py I get some sort of cannot import gdb Import error So then I tried calling "pi": (gdb) pi printf.py Python scripting is not supported in this copy of GDB. So... how do I get Python support in GDB7.8? is it actually not supported? Or should I not call "pi"?

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  • Sync csv file using nodejs

    - by Amit Dugar
    There is a remote csv file that gets updated every second or so. I need to download it(on a Windows machine) ONCE and always sync local file with the remote one. Obviously, downloading the whole file every time is not an option. I need to download only the changes.(something like rsync, rdiff-backup) I searched quite a bit but could not find how I can do this. I am sort of new to nodejs and am using this app as an opportunity to expand my nodejs skills. Also, I am planning to use nodejs and to package it using node-webkit(https://github.com/rogerwang/node-webkit)

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  • Specifying formatting for csv.writer in Python

    - by user248237
    I am using csv.DictWriter to output csv files from a set of dictionaries. I use the following function: def dictlist2file(dictrows, filename, fieldnames, delimiter='\t', lineterminator='\n'): out_f = open(filename, 'w') # Write out header header = delimiter.join(fieldnames) + lineterminator out_f.write(header) # Write out dictionary data = csv.DictWriter(out_f, fieldnames, delimiter=delimiter, lineterminator=lineterminator) data.writerows(dictrows) out_f.close() where dictrows is a list of dictionaries, and fieldnames provides the headers that should be serialized to file. Some of the values in my dictionary list (dictrows) are numeric -- e.g. floats, and I'd like to specify the formatting of these. For example, I might want floats to be serialized with "%.2f" rather than full precision. Ideally, I'd like to specify some kind of mapping that says how to format each type, e.g. {float: "%.2f"} that says that if you see a float, format it with %.2f. Is there an easy way to do this? I don't want to subclass DictWriter or anything complicated like that -- this seems like very generic functionality. How can this be done? The only other solution I can think of is: instead of messing with the formatting of DictWriter, just use the decimal package to specify the decimal precision of floats to be %.2 which will cause to be serialized as such. Don't know if this is a better solution? thanks very much for your help.

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  • Fastest image iteration in Python

    - by Greg
    I am creating a simple green screen app with Python 2.7.4 but am getting quite slow results. I am currently using PIL 1.1.7 to load and iterate the images and saw huge speed-ups changing from the old getpixel() to the newer load() and pixel access object indexing. However the following loop still takes around 2.5 seconds to run for an image of around 720p resolution: def colorclose(Cb_p, Cr_p, Cb_key, Cr_key, tola, tolb): temp = math.sqrt((Cb_key-Cb_p)**2+(Cr_key-Cr_p)**2) if temp < tola: return 0.0 else: if temp < tolb: return (temp-tola)/(tolb-tola) else: return 1.0 .... for x in range(width): for y in range(height): Y, cb, cr = fg_cbcr_list[x, y] mask = colorclose(cb, cr, cb_key, cr_key, tola, tolb) mask = 1 - mask bgr, bgg, bgb = bg_list[x,y] fgr, fgg, fgb = fg_list[x,y] pixels[x,y] = ( (int)(fgr - mask*key_color[0] + mask*bgr), (int)(fgg - mask*key_color[1] + mask*bgg), (int)(fgb - mask*key_color[2] + mask*bgb)) Am I doing anything hugely inefficient here which makes it run so slow? I have seen similar, simpler examples where the loop is replaced by a boolean matrix for instance, but for this case I can't see a way to replace the loop. The pixels[x,y] assignment seems to take the most amount of time but not knowing Python very well I am unsure of a more efficient way to do this. Any help would be appreciated.

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  • Python and csv help

    - by user353064
    I'm trying to create this script that will check the computer host name then search a master list for the value to return a corresponding value in the csv file. Then open another file and do a find an replace. I know this should be easy but haven't done so much in python before. Here is what I have so far... masterlist.txt (tab delimited) Name UID Bob-Smith.local bobs Carmen-Jackson.local carmenj David-Kathman.local davidk Jenn-Roberts.local jennr Here is the script that I have created thus far #GET CLIENT HOST NAME import socket host = socket.gethostname() print host #IMPORT MASTER DATA import csv, sys filename = "masterlist.txt" reader = csv.reader(open(filename, "rU")) #PRINT MASTER DATA for row in reader: print row #SEARCH ON HOSTNAME AND RETURN UID #REPLACE VALUE IN FILE WITH UID #import fileinput #for line in fileinput.FileInput("filetoreplace",inplace=1): # line = line.replace("replacethistext","UID") # print line Right now, it's just set to print the master list. I'm not sure if the list needs to be parsed and placed into a dictionary or what. I really need to figure out how to search the first field for the hostname and then return the field in the second column. Thanks in advance for your help, Aaron

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  • Saving a file in a CSV type in Excel always removes the BOM

    - by rickp
    I've been trying to find a reasonable solution/explanation (unsuccessfully) to find out why Excel defaults to removing the BOM when saving a file to the CSV type. Please forgive me if you find this a duplicate of this question. This handles reading CSV files with non-ASCII encoding, but it doesn't cover saving the file back out (which is where the biggest issue lies). Here is my current situation (which I'm going to gather is common among localized software dealing with Unicode characters and a CSV format): We export data to a CSV format using UTF-16LE, ensuring the BOM is set (0xFFFE). We validate after the file is generated with a Hex editor to ensure it was set correctly. Open the file in Excel (for this example we're exporting Japanese characters) and witness that Excel handles loading the file with the correct encoding. Attempts to save this file will prompt you with a warning message indicating that the file may contain features that may not be compatible with Unicode encoding, but asks if you'd like to save anyway. If you select the Save As dialog, it will immediately ask you to save the file as "Unicode Text" rather than CSV. If you select the "CSV" extension and save the file it removes the BOM (obviously along with all the Japanese characters). Why would this happen? Is there a solution to this problem, or is this a known 'bug'/limitation of Excel? Additionally (as a side issue) it appears that Excel, when loading UTF-16LE encoded CSV files, only uses TAB delimiters. Again, is this another known 'bug'/limitation of Excel?

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  • Saving a file in a CSV type in Excel always removes the BOM

    - by rickp
    I've been trying to find a reasonable solution/explanation (unsuccessfully) to find out why Excel defaults to removing the BOM when saving a file to the CSV type. Please forgive me if you find this a duplicate of this question. This handles reading CSV files with non-ASCII encoding, but it doesn't cover saving the file back out (which is where the biggest issue lies). Here is my current situation (which I'm going to gather is common among localized software dealing with Unicode characters and a CSV format): We export data to a CSV format using UTF-16LE, ensuring the BOM is set (0xFFFE). We validate after the file is generated with a Hex editor to ensure it was set correctly. Open the file in Excel (for this example we're exporting Japanese characters) and witness that Excel handles loading the file with the correct encoding. Attempts to save this file will prompt you with a warning message indicating that the file may contain features that may not be compatible with Unicode encoding, but asks if you'd like to save anyway. If you select the Save As dialog, it will immediately ask you to save the file as "Unicode Text" rather than CSV. If you select the "CSV" extension and save the file it removes the BOM (obviously along with all the Japanese characters). Why would this happen? Is there a solution to this problem, or is this a known 'bug'/limitation of Excel? Additionally (as a side issue) it appears that Excel, when loading UTF-16LE encoded CSV files, only uses TAB delimiters. Again, is this another known 'bug'/limitation of Excel?

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  • Python requests - saving cookie for later url usage

    - by PythonRocks
    I been trying to get a cookie and post it to a url in later use in the program, but I cant seem to get the cookie parameters to work. Right now I have response = requests.get("url") But how exactly do I retrive cookies from this url and post them to a new url (the same cookies). The tutorial in requests is somewhat vague on the topic and gives examples I cannot test. Hope someone can help with further examples. This is python 2.7 btw.

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  • Python Parse regex

    - by Nemo
    Let's say I have string in the form given below: myString={"name", "age", "address", "contacts", "Email"} I need to get all the items of myString into a List using python. Here's what I did r= re.search("myString=\{\"(.+)\", $\}", line) if r: items.append(r.group(1)) print(items) Here line is the variable that holds the content of my text file. What change do I have to make to my regex to get all the items in myString? Please kindly help me out. Thanks.

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  • Handling extra newlines in csv files parsed with Python?

    - by rmihalyi
    I have a CSV file that contains extra newlines in some fields, e.g.: A, B, C, D, E, F 123, 456, tree , very, bla, indigo I tried the following: import csv catalog = csv.reader(open('test.csv', 'rU'), delimiter=",", dialect=csv.excel_tab) for row in catalog: print "Length: ", len(row), row and the result I got was this: Length: 6 ['A', ' B', ' C', ' D', ' E', ' F'] Length: 3 ['123', ' 456', ' tree'] Length: 4 [' ', ' very', ' bla', ' indigo'] Does anyone have any idea how I can quickly remove extraneous newlines? Thanks!

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  • Python DictReader - Skipping rows with missing columns?

    - by victorhooi
    heya, I have a Excel .CSV file I'm attempting to read in with DictReader. All seems to be well, except it seems to omit rows, specifically those with missing columns. Our input looks like: mail,givenName,sn,lorem,ipsum,dolor,telephoneNumber [email protected],ian,bay,3424,8403,2535,+65(2)34523534545 [email protected],mike,gibson,3424,8403,2535,+65(2)34523534545 [email protected],ross,martin,,,,+65(2)34523534545 [email protected],david,connor,,,,+65(2)34523534545 [email protected],chris,call,3424,8403,2535,+65(2)34523534545 So some of the rows have missing lorem/ipsum/dolor columns, and it's just a string of commas for those. We're reading it in with: def read_gd_dump(input_file="blah 20100423.csv"): gd_extract = csv.DictReader(open('blah 20100423.csv'), restval='missing', dialect='excel') return dict([(row['something'], row) for row in gd_extract]) And I checked that "something" (the key for our dict) isn't one of the missing columns, I had originally suspected it might be that. It's one of the columns after that. However, DictReader seems to completely skip over the rows. I tried setting restval to something, didn't seem to make any difference. I can't seem to find anything in Python's CSV docs (http://docs.python.org/library/csv.html) that would explain this behaviour, but I may have misread something. Any ideas? Thanks, Victor

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  • SQL SERVER – Powershell – Importing CSV File Into Database – Video

    - by pinaldave
    Laerte Junior is my very dear friend and Powershell Expert. On my request he has agreed to share Powershell knowledge with us. Laerte Junior is a SQL Server MVP and, through his technology blog and simple-talk articles, an active member of the Microsoft community in Brasil. He is a skilled Principal Database Architect, Developer, and Administrator, specializing in SQL Server and Powershell Programming with over 8 years of hands-on experience. He holds a degree in Computer Science, has been awarded a number of certifications (including MCDBA), and is an expert in SQL Server 2000 / SQL Server 2005 / SQL Server 2008 technologies. Let us read the blog post in his own words. I was reading an excellent post from my great friend Pinal about loading data from CSV files, SQL SERVER – Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video,   to SQL Server and was honored to write another guest post on SQL Authority about the magic of the PowerShell. The biggest stuff in TechEd NA this year was PowerShell. Fellows, if you still don’t know about it, it is better to run. Remember that The Core Servers to SQL Server are the future and consequently the Shell. You don’t want to be out of this, right? Let’s see some PowerShell Magic now. To start our tour, first we need to download these two functions from Powershell and SQL Server Master Jedi Chad Miller.Out-DataTable and Write-DataTable. Save it in a module and add it in your profile. In my case, the module is called functions.psm1. To have some data to play, I created 10 csv files with the same content. I just put the SQL Server Errorlog into a csv file and created 10 copies of it. #Just create a CSV with data to Import. Using SQLErrorLog [reflection.assembly]::LoadWithPartialName(“Microsoft.SqlServer.Smo”) $ServerInstance=new-object (“Microsoft.SqlServer.Management.Smo.Server“) $Env:Computername $ServerInstance.ReadErrorLog() | export-csv-path“c:\SQLAuthority\ErrorLog.csv”-NoTypeInformation for($Count=1;$Count-le 10;$count++)  {       Copy-Item“c:\SQLAuthority\Errorlog.csv”“c:\SQLAuthority\ErrorLog$($count).csv” } Now in my path c:\sqlauthority, I have 10 csv files : Now it is time to create a table. In my case, the SQL Server is called R2D2 and the Database is SQLServerRepository and the table is CSV_SQLAuthority. CREATE TABLE [dbo].[CSV_SQLAuthority]( [LogDate] [datetime] NULL, [Processinfo] [varchar](20) NULL, [Text] [varchar](MAX) NULL ) Let’s play a little bit. I want to import synchronously all csv files from the path to the table: #Importing synchronously $DataImport=Import-Csv-Path ( Get-ChildItem“c:\SQLAuthority\*.csv”) $DataTable=Out-DataTable-InputObject$DataImport Write-DataTable-ServerInstanceR2D2-DatabaseSQLServerRepository-TableNameCSV_SQLAuthority-Data$DataTable Very cool, right? Let’s do it asynchronously and in background using PowerShell  Jobs: #If you want to do it to all asynchronously Start-job-Name‘ImportingAsynchronously‘ ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock {    ` $DataImport=Import-Csv-Path ( Get-ChildItem“c:\SQLAuthority\*.csv”) $DataTable=Out-DataTable-InputObject$DataImport Write-DataTable   -ServerInstance“R2D2″`                   -Database“SQLServerRepository“`                   -TableName“CSV_SQLAuthority“`                   -Data$DataTable             } Oh, but if I have csv files that are large in size and I want to import each one asynchronously. In this case, this is what should be done: Get-ChildItem“c:\SQLAuthority\*.csv” | % { Start-job-Name“$($_)” ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock { $DataImport=Import-Csv-Path$args[0]                $DataTable=Out-DataTable-InputObject$DataImport                Write-DataTable-ServerInstance“R2D2″`                               -Database“SQLServerRepository“`                               -TableName“CSV_SQLAuthority“`                               -Data$DataTable             } -ArgumentList$_.fullname } How cool is that? Let’s make the funny stuff now. Let’s schedule it on an SQL Server Agent Job. If you are using SQL Server 2012, you can use the PowerShell Job Step. Otherwise you need to use a CMDexec job step calling PowerShell.exe. We will use the second option. First, create a ps1 file called ImportCSV.ps1 with the script above and save it in a path. In my case, it is in c:\temp\automation. Just add the line at the end: Get-ChildItem“c:\SQLAuthority\*.csv” | % { Start-job-Name“$($_)” ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock { $DataImport=Import-Csv-Path$args[0]                $DataTable=Out-DataTable-InputObject$DataImport                Write-DataTable-ServerInstance“R2D2″`                               -Database“SQLServerRepository“`                               -TableName“CSV_SQLAuthority“`                               -Data$DataTable             } -ArgumentList$_.fullname } Get-Job | Wait-Job | Out-Null Remove-Job -State Completed Why? See my post Dooh PowerShell Trick–Running Scripts That has Posh Jobs on a SQL Agent Job Remember, this trick is for  ALL scripts that will use PowerShell Jobs and any kind of schedule tool (SQL Server agent, Windows Schedule) Create a Job Called ImportCSV and a step called Step_ImportCSV and choose CMDexec. Then you just need to schedule or run it. I did a short video (with matching good background music) and you can see it at: That’s it guys. C’mon, join me in the #PowerShellLifeStyle. You will love it. If you want to check what we can do with PowerShell and SQL Server, don’t miss Laerte Junior LiveMeeting on July 18. You can have more information in : LiveMeeting VC PowerShell PASS–Troubleshooting SQL Server With PowerShell–English Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology, Video Tagged: Powershell

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  • python metaprogramming

    - by valya
    I'm trying to archive a task which turns out to be a bit complicated since I'm not very good at Python metaprogramming. I want to have a module locations with function get_location(name), which returns a class defined in a folder locations/ in the file with the name passed to function. Name of a class is something like NameLocation. So, my folder structure: program.py locations/ __init__.py first.py second.py program.py will be smth with with: from locations import get_location location = get_location('first') and the location is a class defined in first.py smth like this: from locations import Location # base class for all locations, defined in __init__ (?) class FirstLocation(Location): pass etc. Okay, I've tried a lot of import and getattribute statements but now I'm bored and surrender. How to archive such behaviour?

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  • bad request error 400 while using python requests.post function

    - by Toussah
    I'm trying to make a simple post request via the requests library of Python and I get a bad request error (400) while my url is supposedly correct since I can use it to perform a get. I'm very new in REST requests, I read many tutorials and documentation but I guess there are still things I don't get so my error could be basic. Maybe a lack of understanding on the type of url I'm supposed to send via POST. Here my code : import requests v_username = "username" v_password = "password" v_headers = {'content-type':'application/rdf+xml'} url = 'https://my.url' params = {'param': 'val_param'} payload = {'data': 'my_data'} r = requests.post(url, params = params, auth=(v_username, v_password), data=payload, headers=v_headers, verify=False) print r I used the example of the requests documentation.

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  • dump csv from sqlalchemy

    - by afilatun
    For some reason, I want to dump a table from a database (sqlite3) in the form of a csv file. I'm using a python script with elixir (based on sqlalchemy) to modify the database. I was wondering if there is any way to dump the table I use to csv. I've seen sqlalchemy serializer but it doesn't seem to be what I want. Am I doing it wrong? Should I call the sqlite3 python module after closing my sqlalchemy session to dump to a file instead? Or should I use something homemade?

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  • Python Parse CSV Correctly

    - by cornerstone
    I am very new to Python. I want to parse a csv file such that it will recognize quoted values - For example 1997,Ford,E350,"Super, luxurious truck" should be split as ('1997', 'Ford', 'E350', 'Super, luxurious truck') and NOT ('1997', 'Ford', 'E350', '"Super', ' luxurious truck"') the above is what I get if I use something like str.split(). How do I do this? Also would it be best to store these values in an array or some other data structure? because after I get these values from the csv I want to be able to easily choose, lets say any two of the columns and store it as another array or some other data structure. Thanks in advance.

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  • Wierd characters in exported csv files when converting

    - by Ahue
    Hey guys, I came across a problem I cannot solve on my own concerning the downloadable csv formatted trends data files from Google Insights for Search. I'm to lazy to reformat the files I4S gives me manually what means: Extracting the section with the actual trends data and reformatting the columns so that I can use it with a modelling program I do for school. So I wrote a tiny script the should do the work for me: Taking a file, do some magic and give me a new file in proper format. What it's supposed to do is reading the file contents, extracting the trends section, splitting it by newlines, splitting each line and then reorder the columns and maybe reformat them. When looking at a untouched I4S csv file it looks normal containing CR LF caracters at line breaks (maybe thats only because I'm using Windows). When just reading the contents and then writing them to a new file using the script wierd asian characters appear between CR and LF. I tried the script with a manually written similar looking file and even tried a csv file from Google Trends and it works fine. I use Python and the script (snippet) I used for the following example looks like this: # Read from an input file file = open(file,"r") contents = file.read() file.close() cfile = open("m.log","w+") cfile.write(contents) cfile.close() Has anybody an idea why those characters appear??? Thank you for you help! I'll give you and example: First few lines of I4S csv file: Web Search Interest: foobar Worldwide; 2004 - present Interest over time Week foobar 2004-01-04 - 2004-01-10 44 2004-01-11 - 2004-01-17 44 2004-01-18 - 2004-01-24 37 2004-01-25 - 2004-01-31 40 2004-02-01 - 2004-02-07 49 2004-02-08 - 2004-02-14 51 2004-02-15 - 2004-02-21 45 2004-02-22 - 2004-02-28 61 2004-02-29 - 2004-03-06 51 2004-03-07 - 2004-03-13 48 2004-03-14 - 2004-03-20 50 2004-03-21 - 2004-03-27 56 2004-03-28 - 2004-04-03 59 Output file when reading and writing contents: Web Search Interest: foobar ??????????? ? ? ? ????????? ????????? ???? ?????? Week foobar ?? ?? ?? ? ? ? ?? ??? ????? 2004-01-11 - 2004-01-17 44 ?? ?? ???? ? ? ?? ????????? 2004-01-25 - 2004-01-31 40 ?? ?? ?? ? ? ? ?? ?? ?????? 2004-02-08 - 2004-02-14 51 ?? ?? ???? ? ? ?? ????????? 2004-02-22 - 2004-02-28 61 ?? ?? ???? ? ? ?? ?? ?????? 2004-03-07 - 2004-03-13 48 ?? ?? ???? ? ? ?? ??? ?? ?? 2004-03-21 - 2004-03-27 56 ?? ?? ???? ? ? ?? ?? ?????? 2004-04-04 - 2004-04-10 69 ?? ?? ???? ? ? ?? ????????? 2004-04-18 - 2004-04-24 51 ?? ?? ???? ? ? ?? ?? ?????? 2004-05-02 - 2004-05-08 56 ?? ?? ?? ? ? ? ?? ????????? 2004-05-16 - 2004-05-22 54 ?? ?? ???? ? ? ?? ????????? 2004-05-30 - 2004-06-05 74 ?? ?? ?? ? ? ? ?? ????????? 2004-06-13 - 2004-06-19 50 ?? ?? ??? ? ? ?? ????????? 2004-06-27 - 2004-07-03 58 ?? ?? ?? ? ? ? ?? ??? ????? 2004-07-11 - 2004-07-17 59 ?? ?? ???? ? ? ?? ?????????

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  • Bit by bit comparison of using Java or Python for unit testing frameworks and Selenium

    - by Anirudh
    Currently we are in the process of finalizing which language out of Java, Python should be used for Automation using selenium webdriver and a suitable unit testing frameworks. I have made use of Junit, TestNG and webdriver while using with Java and have designed frameworks without much fuss before. I am new to python though I came across pyhton's unit testing frameworks like unittest, pyunit, nose e.t.c but I have doubts if they would be as successful as testNG or Java. I would like to analyze point by point when used with selenium webdriver as below: 1)I have read that as Python is an interpreted language hence it's execution is slower, so say if I have to run 1000 test cases which take about 6 hours to run in Java, would python take considerably longer time for the same test cases like 8 hours? 2)Can the Python unit testing framework be as flexible as a Java unit testing framework like testNG in terms or Grouping the tests, parallel execution, skipping test. e.t.c 3)Also one point that I think of is that Python with selenium webdriver doeasn't have as big or learned community as we have for Java with webdriver, say if I run into trouble with something I am more likely to find an answer for Java as compared to python? 4)Somewhat related to point 3, is it safe to rely on tools, plugins or even webderiver's python's binding as a continuously well maintained? 5)One major drawback as I see while using python's unit testing framework is lack of boilerplate code or libraries for nicely illustrative HTML reports preferably historical reports with Pie charts, bar graphs and timelines as we have in case of Java like Allure, TestNG's default reports, reportNG or Junit reports with the help of ANT as shown below Allure Reports Junit Historical reports Also I would like to emphasize on the fact if there is a way for one to write the framework in java and make libraries or utilities according to out application in webdriver which can easily be called or integrated in with python code or modules? That would actually solve the problem for us as the client would be able to use the code we write in Java and make use of the same or call it from their python modules?

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  • Python - Calling a non python program from python?

    - by Seafoid
    Hi, I am currently struggling to call a non python program from a python script. I have a ~1000 files that when passed through this C++ program will generate ~1000 outputs. Each output file must have a distinct name. The command I wish to run is of the form: program_name -input -output -o1 -o2 -o3 To date I have tried: import os cwd = os.getcwd() files = os.listdir(cwd) required_files = [] for i in file: if i.endswith('.ttp'): required_files.append(i) So, I have an array of the neccesary files. My problem - how do I iterate over the array and for each entry, pass it to the above command (program_name) as an argument and specify a unique output id for each file? Much appreciated, S :-)

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  • How can i make changes to this file Encoding?

    - by SuperUserMan
    I have these 3 files 21/08/2014 07:15 PM 122 Tw2AWK.csv 21/08/2014 07:15 PM 125 Tw2Notepad.csv 21/08/2014 07:15 PM 119 Tw2REPL.csv C:\myfilesfile Tw2AWK.csv TwREPL.csv Tw2Notepad.csv Tw2AWK.csv; UTF-8 Unicode text, with CRLF line terminators Tw2REPL.csv; UTF-8 Unicode text Tw2Notepad.csv; UTF-8 Unicode (with BOM) text, with CRLF line terminators HEX of these files is as follows C:\myfilesxxd -p Tw2REPL.csv 0a222344656c686947616e675261706520776173206120736d616c6c2069 6e636964656e7420746f2023536d616c6c5261706973744a6169746c6579 20646e61696e6469612e636f6d2f696e6469612f7265706f72742d69e280 a6207069632e747769747465722e636f6d2f6762565070776637744f22 C:\myfilesxxd -p Tw2AWK.csv 0d0a222344656c686947616e675261706520776173206120736d616c6c20 696e636964656e7420746f2023536d616c6c5261706973744a6169746c65 7920646e61696e6469612e636f6d2f696e6469612f7265706f72742d69e2 80a6207069632e747769747465722e636f6d2f6762565070776637744f22 0d0a C:\myfilesxxd -p Tw2Notepad.csv efbbbf0d0a222344656c686947616e675261706520776173206120736d61 6c6c20696e636964656e7420746f2023536d616c6c5261706973744a6169 746c657920646e61696e6469612e636f6d2f696e6469612f7265706f7274 2d69e280a6207069632e747769747465722e636f6d2f6762565070776637 744f220d0a I want Tw2REPL.csv to look like Tw2Notepad.csv How can I do it? NOTE: I have do this all via command line (batch) . I can use any 3rd party standalone exe's though. I am on Windows XP Please help, its very important for me

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  • Python regex to parse text file, get the items in list and count the list

    - by Nemo
    I have a text file which contains some data. I m particularly interested in finding the count of the number of items in v_dims v_dims pattern in my text file looks like this : v_dims={ "Sales", "Product Family", "Sales Organization", "Region", "Sales Area", "Sales office", "Sales Division", "Sales Person", "Sales Channel", "Sales Order Type", "Sales Number", "Sales Person", "Sales Quantity", "Sales Amount" } So I m thinking of getting all the elements in v_dims and dumping them out in a Python list. Then compute the len(mylist) to get the count of the items. The challenge is in getting all the elements of v_dims from my text file and putting them in an empty list. I m particularly interested in items in v_dims in my text file. The text file has data in the form of v_dims pattern i showed in my original post. Some data has nested patterns of v_dims. Thanks. Here's what I have tried and failed. Any help is appreciated. TIA. import re fname = "C:\Users\XXXX\Test.mrk" with open(fname, "r") as fo: content_as_string = fo.read() match = re.findall(r'v_dims={\"(.+?)\"}',content_as_string) Though I have a big text file, Here's a snippet of what's the structure of my text file version "1"; // Computer generated object language file object 'MRKR' "Main" { Data_Type=2, HeaderBlock={ Version_String="6.3 (25)" }, Printer_Info={ Orientation=0, Page_Width=8.50000000, Page_Height=11.00000000, Page_Header="", Page_Footer="", Margin_type=0, Top_Margin=0.50000000, Left_Margin=0.50000000, Bottom_Margin=0.50000000, Right_Margin=0.50000000 }, Marker_Options={ Close_All="TRUE", Hide_Console="FALSE", Console_Left="FALSE", Console_Width=217, Main_Style="Maximized", MDI_Rect={ 0, 0, 892, 1063 } }, Dives={ { Dive="A", Windows={ { View_Index=0, Window_Info={ Window_Rect={ 0, -288, 400, 1008 }, Window_Style="Maximized Front", Window_Name="Theater [Previous Qtr Diveplan-Dive A]" }, Dependent_bool="FALSE", Colset={ Dive_Type="Normal", Dimension_Name="Theater", Action_List={ Actions={ { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater" }, Key_Indexes={ { "AMERICAS" } } }, { Action_Type="Focus", Focus_Rows="True" }, { Action_Type="Dimensions", v_dims={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag", "Product Item Family" }, Xtab_Bool="False", Xtab_Flip="False" }, { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag" }, Key_Indexes={ { "AMERICAS", "ATMOS", "Latin America CS Division", "37000 CS Region", "Mexico", "", "", "", "", "DIRECT", "EMC", "N", "0" } } } } }, Num_Palette_cols=0, Num_Palette_rows=0 }, Format={ Window_Type="Tabular", Tabular={ Num_row_labels=8 } } } } } }, Widget_Set={ Widget_Layout="Vertical", Go_Button=1, Picklist_Width=0, Sort_Subset_Dimensions="TRUE", Order={ } }, Views={ { Data_Type=1, dbname="Previous Qtr Diveplan", diveline_dbname="Current Qtr Diveplan", logical_name="Current Qtr Diveplan", cols={ { name="Total TSS installs", column_type="Calc[Total TSS installs]", output_type="Number", format_string="." }, { name="TSS Valid Connectivity Records", column_type="Calc[TSS Valid Connectivity Records]", output_type="Number", format_string="." }, { name="% TSS Connectivity Record", column_type="Calc[% TSS Connectivity Record]", output_type="Number" }, { name="TSS Not Applicable", column_type="Calc[TSS Not Applicable]", output_type="Number", format_string="." }, { name="TSS Customer Refusals", column_type="Calc[TSS Customer Refusals]", output_type="Number", format_string="." }, { name="% TSS Refusals", column_type="Calc[% TSS Refusals]", output_type="Number" }, { name="TSS Eligible for Physical Connectivity", column_type="Calc[TSS Eligible for Physical Connectivity]", output_type="Number", format_string="." }, { name="TSS Boxes with Physical Connectivty", column_type="Calc[TSS Boxes with Physical Connectivty]", output_type="Number", format_string="." }, { name="% TSS Physical Connectivity", column_type="Calc[% TSS Physical Connectivity]", output_type="Number" } }, dim_cols={ { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Connect In Type", column_type="Dimension[Connect In Type]", output_type="None" }, { name="Connect Home Type", column_type="Dimension[Connect Home Type]", output_type="None" }, { name="SymmConnect Enabled", column_type="Dimension[SymmConnect Enabled]", output_type="None" }, { name="Theater", column_type="Dimension[Theater]", output_type="None" }, { name="Division", column_type="Dimension[Division]", output_type="None" }, { name="Region", column_type="Dimension[Region]", output_type="None" }, { name="Sales Order Number", column_type="Dimension[Sales Order Number]", output_type="None" }, { name="Product Item Family", column_type="Dimension[Product Item Family]", output_type="None" }, { name="Item Serial Number", column_type="Dimension[Item Serial Number]", output_type="None" }, { name="Sales Order Deal Number", column_type="Dimension[Sales Order Deal Number]", output_type="None" }, { name="Item Install Date", column_type="Dimension[Item Install Date]", output_type="None" }, { name="SYR Last Dial Home Date", column_type="Dimension[SYR Last Dial Home Date]", output_type="None" }, { name="Maintained By Group", column_type="Dimension[Maintained By Group]", output_type="None" }, { name="PS Flag", column_type="Dimension[PS Flag]", output_type="None" }, { name="Connect Home Refusal Reason", column_type="Dimension[Connect Home Refusal Reason]", output_type="None", col_width=177 }, { name="Cust Name", column_type="Dimension[Cust Name]", output_type="None" }, { name="Sales Order Channel Type", column_type="Dimension[Sales Order Channel Type]", output_type="None" }, { name="Sales Order Type", column_type="Dimension[Sales Order Type]", output_type="None" }, { name="Part Model Key", column_type="Dimension[Part Model Key]", output_type="None" }, { name="Ship Date", column_type="Dimension[Ship Date]", output_type="None" }, { name="Model Number", column_type="Dimension[Model Number]", output_type="None" }, { name="Item Description", column_type="Dimension[Item Description]", output_type="None" }, { name="Customer Classification", column_type="Dimension[Customer Classification]", output_type="None" }, { name="CS Customer Name", column_type="Dimension[CS Customer Name]", output_type="None" }, { name="Install At Customer Number", column_type="Dimension[Install At Customer Number]", output_type="None" }, { name="Install at Country Name", column_type="Dimension[Install at Country Name]", output_type="None" }, { name="TLA Serial Number", column_type="Dimension[TLA Serial Number]", output_type="None" }, { name="Product Version", column_type="Dimension[Product Version]", output_type="None" }, { name="Avalanche Flag", column_type="Dimension[Avalanche Flag]", output_type="None" }, { name="Product Family", column_type="Dimension[Product Family]", output_type="None" }, { name="Project Number", column_type="Dimension[Project Number]", output_type="None" }, { name="PROJECT_STATUS", column_type="Dimension[PROJECT_STATUS]", output_type="None" } }, Available_Columns={ "Total TSS installs", "TSS Valid Connectivity Records", "% TSS Connectivity Record", "TSS Not Applicable", "TSS Customer Refusals", "% TSS Refusals", "TSS Eligible for Physical Connectivity", "TSS Boxes with Physical Connectivty", "% TSS Physical Connectivity", "Total Installs", "All Boxes with Valid Connectivty Record", "% All Connectivity Record", "Overall Refusals", "Overall Refusals %", "All Eligible for Physical Connectivty", "Boxes with Physical Connectivity", "% All with Physical Conectivity" }, Remaining_columns={ { name="Total Installs", column_type="Calc[Total Installs]", output_type="Number", format_string="." }, { name="All Boxes with Valid Connectivty Record", column_type="Calc[All Boxes with Valid Connectivty Record]", output_type="Number", format_string="." }, { name="% All Connectivity Record", column_type="Calc[% All Connectivity Record]", output_type="Number" }, { name="Overall Refusals", column_type="Calc[Overall Refusals]", output_type="Number", format_string="." }, { name="Overall Refusals %", column_type="Calc[Overall Refusals %]", output_type="Number" }, { name="All Eligible for Physical Connectivty", column_type="Calc[All Eligible for Physical Connectivty]", output_type="Number" }, { name="Boxes with Physical Connectivity", column_type="Calc[Boxes with Physical Connectivity]", output_type="Number" }, { name="% All with Physical Conectivity", column_type="Calc[% All with Physical Conectivity]", output_type="Number" } }, calcs={ { name="Total TSS installs", definition="Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Valid Connectivity Records", definition="Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% TSS Connectivity Record", definition="Total[PS Boxes w/ valid connectivity record (1=yes)] /Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Not Applicable", definition="Total[Bozes w/ valid connectivity record (1=yes)]-Total[Boxes Eligible (1=yes)]-Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="TSS Customer Refusals", definition="Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="% TSS Refusals", definition="Total[TSS Refusals]/Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="TSS Eligible for Physical Connectivity", definition="Total[TSS Eligible]-Total[Exception]", ts_flag="Not TS Calc" }, { name="TSS Boxes with Physical Connectivty", definition="Total[PS Physical Connectivity] - Total[PS Physical Connectivity, SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% TSS Physical Connectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" }, { name="Total Installs", definition="Total[Total Installs]", ts_flag="Not TS Calc" }, { name="All Boxes with Valid Connectivty Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% All Connectivity Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]/Total[Total Installs]", ts_flag="Not TS Calc" }, { name="Overall Refusals", definition="Total[Overall Refusals]", ts_flag="Not TS Calc" }, { name="Overall Refusals %", definition="Total[Overall Refusals]/Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="All Eligible for Physical Connectivty", definition="Total[Boxes Eligible (1=yes)]-Total[Exception]", ts_flag="Not TS Calc" }, { name="Boxes with Physical Connectivity", definition="Total[Boxes w/ phys conn]-Total[Boxes w/ phys conn,SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% All with Physical Conectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" } }, merge_type="consolidate", merge_dbs={ { dbname="connectivityallproducts.mdl", diveline_dbname="/DI_PSREPORTING/connectivityallproducts.mdl" } }, skip_constant_columns="FALSE", categories={ { name="Geography", dimensions={ "Theater", "Division", "Region", "Install at Country Name" } }, { name="Mappings and Flags", dimensions={ "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "Customer Installable", "PS Flag", "Top Level Flag", "Avalanche Flag" } }, { name="Product Information", dimensions={ "Product Family", "Product Item Family", "Product Version", "Item Description" } }, { name="Sales Order Info", dimensions={ "Sales Order Deal Number", "Sales Order Number", "Sales Order Type" } }, { name="Dates", dimensions={ "Item Install Date", "Ship Date", "SYR Last Dial Home Date" } }, { name="Details", dimensions={ "Item Serial Number", "TLA Serial Number", "Part Model Key", "Model Number" } }, { name="Customer Infor", dimensions={ "CS Customer Name", "Install At Customer Number", "Customer Classification", "Cust Name" } }, { name="Other Dimensions", dimensions={ "Model" } } }, Maintain_Category_Order="FALSE", popup_info="false" } } };

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